Compact-Range RCS Measurements and Modeling of Small Drones at 15 GHz\n and 25 GHz
Martins Ezuma, Mark Funderburk, İsmail Güvenç
Abstract
Open-access reader
Martins Ezuma, Mark Funderburk, İsmail Güvenç
Abstract
Open-access reader
The knowledge of the radar signature of aerial targets, such as drones, is\ncritical in designing an effective radar detection system. It is a challenging\ntask to measure the radar cross-section (RCS) of small drones. This paper\ndescribes a compact-range approach for measuring the RCS of small drones at 15\nGHz and 25 GHz. The measurement results show that the average RCS of the three\nsmall drones varies with the radar frequency with higher reflections observed\naround certain directions. Moreover, the results show that for each drone, the\nRCS at 25 GHz is higher than the RCS at 15 GHz. Besides,\ninformation-theoretical based model selection for the RCS data is carried using\nthe Akaike information criterion (AIC). We find that the generalized extreme\nvalue distribution is a good fit for modeling the RCS of small drones.\n
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The knowledge of the radar signature of aerial targets, such as drones, is\ncritical in designing an effective radar detection system. It is a challenging\ntask to measure the radar cross-section (RCS) of small drones. This paper\ndescribes a compact-range approach for measuring the RCS of small drones at 15\nGHz and 25 GHz. The measurement results show that the average RCS of the three\nsmall drones varies with the radar frequency with higher reflections observed\naround certain directions. Moreover, the results show that for each drone, the\nRCS at 25 GHz is higher than the RCS at 15 GHz. Besides,\ninformation-theoretical based model selection for the RCS data is carried using\nthe Akaike information criterion (AIC). We find that the generalized extreme\nvalue distribution is a good fit for modeling the RCS of small drones.\n
Key concepts: Drone, Radar cross-section, Radar, Akaike information criterion, Measure (data warehouse), Range (aeronautics), Computer science, Remote sensing